A multitude of interesting relations between entities are unknown in various scenarios or applications. A basic motivation is to predict such relations and dependencies. Such applications refer to, e.g., the medical domain, bioinformatics or social networks. Data analysis and support of decision making is a key objective based on the huge amounts of data available.
Three common approaches for deriving or predicting instantiated relations are information extraction, deductive reasoning and machine learning.
Information extraction (IE) uses sub-symbolic unstructured sensory information, e.g., in form of texts or images, and extracts statements using various methods ranging from simple classifiers to the most sophisticated Natural Language Processing (NLP) approaches (see, e.g., http://en.wikipedia.org/wiki/Information_extraction).
Deductive reasoning is based on a symbolic representation and derives new statements from logical axioms (see, e.g., http://en.wikipedia.org/wiki/Deductive_reasoning).
Machine learning (ML) can both support information extraction by deriving symbolic representations from sensory data, e.g., via classification, and can support deductive reasoning by exploiting regularities in structured data (see, e.g., http://en.wikipedia.org/wiki/Machine_learning).